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Grounded-Segment-Anything
/
transformers_4_35_0
/models
/audio_spectrogram_transformer
/convert_audio_spectrogram_transformer_original_to_pytorch.py
| # coding=utf-8 | |
| # Copyright 2022 The HuggingFace Inc. team. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """Convert Audio Spectrogram Transformer checkpoints from the original repository. URL: https://github.com/YuanGongND/ast""" | |
| import argparse | |
| import json | |
| from pathlib import Path | |
| import torch | |
| import torchaudio | |
| from datasets import load_dataset | |
| from huggingface_hub import hf_hub_download | |
| from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification | |
| from transformers.utils import logging | |
| logging.set_verbosity_info() | |
| logger = logging.get_logger(__name__) | |
| def get_audio_spectrogram_transformer_config(model_name): | |
| config = ASTConfig() | |
| if "10-10" in model_name: | |
| pass | |
| elif "speech-commands" in model_name: | |
| config.max_length = 128 | |
| elif "12-12" in model_name: | |
| config.time_stride = 12 | |
| config.frequency_stride = 12 | |
| elif "14-14" in model_name: | |
| config.time_stride = 14 | |
| config.frequency_stride = 14 | |
| elif "16-16" in model_name: | |
| config.time_stride = 16 | |
| config.frequency_stride = 16 | |
| else: | |
| raise ValueError("Model not supported") | |
| repo_id = "huggingface/label-files" | |
| if "speech-commands" in model_name: | |
| config.num_labels = 35 | |
| filename = "speech-commands-v2-id2label.json" | |
| else: | |
| config.num_labels = 527 | |
| filename = "audioset-id2label.json" | |
| id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) | |
| id2label = {int(k): v for k, v in id2label.items()} | |
| config.id2label = id2label | |
| config.label2id = {v: k for k, v in id2label.items()} | |
| return config | |
| def rename_key(name): | |
| if "module.v" in name: | |
| name = name.replace("module.v", "audio_spectrogram_transformer") | |
| if "cls_token" in name: | |
| name = name.replace("cls_token", "embeddings.cls_token") | |
| if "dist_token" in name: | |
| name = name.replace("dist_token", "embeddings.distillation_token") | |
| if "pos_embed" in name: | |
| name = name.replace("pos_embed", "embeddings.position_embeddings") | |
| if "patch_embed.proj" in name: | |
| name = name.replace("patch_embed.proj", "embeddings.patch_embeddings.projection") | |
| # transformer blocks | |
| if "blocks" in name: | |
| name = name.replace("blocks", "encoder.layer") | |
| if "attn.proj" in name: | |
| name = name.replace("attn.proj", "attention.output.dense") | |
| if "attn" in name: | |
| name = name.replace("attn", "attention.self") | |
| if "norm1" in name: | |
| name = name.replace("norm1", "layernorm_before") | |
| if "norm2" in name: | |
| name = name.replace("norm2", "layernorm_after") | |
| if "mlp.fc1" in name: | |
| name = name.replace("mlp.fc1", "intermediate.dense") | |
| if "mlp.fc2" in name: | |
| name = name.replace("mlp.fc2", "output.dense") | |
| # final layernorm | |
| if "audio_spectrogram_transformer.norm" in name: | |
| name = name.replace("audio_spectrogram_transformer.norm", "audio_spectrogram_transformer.layernorm") | |
| # classifier head | |
| if "module.mlp_head.0" in name: | |
| name = name.replace("module.mlp_head.0", "classifier.layernorm") | |
| if "module.mlp_head.1" in name: | |
| name = name.replace("module.mlp_head.1", "classifier.dense") | |
| return name | |
| def convert_state_dict(orig_state_dict, config): | |
| for key in orig_state_dict.copy().keys(): | |
| val = orig_state_dict.pop(key) | |
| if "qkv" in key: | |
| key_split = key.split(".") | |
| layer_num = int(key_split[3]) | |
| dim = config.hidden_size | |
| if "weight" in key: | |
| orig_state_dict[ | |
| f"audio_spectrogram_transformer.encoder.layer.{layer_num}.attention.attention.query.weight" | |
| ] = val[:dim, :] | |
| orig_state_dict[ | |
| f"audio_spectrogram_transformer.encoder.layer.{layer_num}.attention.attention.key.weight" | |
| ] = val[dim : dim * 2, :] | |
| orig_state_dict[ | |
| f"audio_spectrogram_transformer.encoder.layer.{layer_num}.attention.attention.value.weight" | |
| ] = val[-dim:, :] | |
| else: | |
| orig_state_dict[ | |
| f"audio_spectrogram_transformer.encoder.layer.{layer_num}.attention.attention.query.bias" | |
| ] = val[:dim] | |
| orig_state_dict[ | |
| f"audio_spectrogram_transformer.encoder.layer.{layer_num}.attention.attention.key.bias" | |
| ] = val[dim : dim * 2] | |
| orig_state_dict[ | |
| f"audio_spectrogram_transformer.encoder.layer.{layer_num}.attention.attention.value.bias" | |
| ] = val[-dim:] | |
| else: | |
| orig_state_dict[rename_key(key)] = val | |
| return orig_state_dict | |
| def remove_keys(state_dict): | |
| ignore_keys = [ | |
| "module.v.head.weight", | |
| "module.v.head.bias", | |
| "module.v.head_dist.weight", | |
| "module.v.head_dist.bias", | |
| ] | |
| for k in ignore_keys: | |
| state_dict.pop(k, None) | |
| def convert_audio_spectrogram_transformer_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub=False): | |
| """ | |
| Copy/paste/tweak model's weights to our Audio Spectrogram Transformer structure. | |
| """ | |
| config = get_audio_spectrogram_transformer_config(model_name) | |
| model_name_to_url = { | |
| "ast-finetuned-audioset-10-10-0.4593": ( | |
| "https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1" | |
| ), | |
| "ast-finetuned-audioset-10-10-0.450": ( | |
| "https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1" | |
| ), | |
| "ast-finetuned-audioset-10-10-0.448": ( | |
| "https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1" | |
| ), | |
| "ast-finetuned-audioset-10-10-0.448-v2": ( | |
| "https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1" | |
| ), | |
| "ast-finetuned-audioset-12-12-0.447": ( | |
| "https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1" | |
| ), | |
| "ast-finetuned-audioset-14-14-0.443": ( | |
| "https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1" | |
| ), | |
| "ast-finetuned-audioset-16-16-0.442": ( | |
| "https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1" | |
| ), | |
| "ast-finetuned-speech-commands-v2": ( | |
| "https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1" | |
| ), | |
| } | |
| # load original state_dict | |
| checkpoint_url = model_name_to_url[model_name] | |
| state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu") | |
| # remove some keys | |
| remove_keys(state_dict) | |
| # rename some keys | |
| new_state_dict = convert_state_dict(state_dict, config) | |
| # load 🤗 model | |
| model = ASTForAudioClassification(config) | |
| model.eval() | |
| model.load_state_dict(new_state_dict) | |
| # verify outputs on dummy input | |
| # source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62 | |
| mean = -4.2677393 if "speech-commands" not in model_name else -6.845978 | |
| std = 4.5689974 if "speech-commands" not in model_name else 5.5654526 | |
| max_length = 1024 if "speech-commands" not in model_name else 128 | |
| feature_extractor = ASTFeatureExtractor(mean=mean, std=std, max_length=max_length) | |
| if "speech-commands" in model_name: | |
| dataset = load_dataset("speech_commands", "v0.02", split="validation") | |
| waveform = dataset[0]["audio"]["array"] | |
| else: | |
| filepath = hf_hub_download( | |
| repo_id="nielsr/audio-spectogram-transformer-checkpoint", | |
| filename="sample_audio.flac", | |
| repo_type="dataset", | |
| ) | |
| waveform, _ = torchaudio.load(filepath) | |
| waveform = waveform.squeeze().numpy() | |
| inputs = feature_extractor(waveform, sampling_rate=16000, return_tensors="pt") | |
| # forward pass | |
| outputs = model(**inputs) | |
| logits = outputs.logits | |
| if model_name == "ast-finetuned-audioset-10-10-0.4593": | |
| expected_slice = torch.tensor([-0.8760, -7.0042, -8.6602]) | |
| elif model_name == "ast-finetuned-audioset-10-10-0.450": | |
| expected_slice = torch.tensor([-1.1986, -7.0903, -8.2718]) | |
| elif model_name == "ast-finetuned-audioset-10-10-0.448": | |
| expected_slice = torch.tensor([-2.6128, -8.0080, -9.4344]) | |
| elif model_name == "ast-finetuned-audioset-10-10-0.448-v2": | |
| expected_slice = torch.tensor([-1.5080, -7.4534, -8.8917]) | |
| elif model_name == "ast-finetuned-audioset-12-12-0.447": | |
| expected_slice = torch.tensor([-0.5050, -6.5833, -8.0843]) | |
| elif model_name == "ast-finetuned-audioset-14-14-0.443": | |
| expected_slice = torch.tensor([-0.3826, -7.0336, -8.2413]) | |
| elif model_name == "ast-finetuned-audioset-16-16-0.442": | |
| expected_slice = torch.tensor([-1.2113, -6.9101, -8.3470]) | |
| elif model_name == "ast-finetuned-speech-commands-v2": | |
| expected_slice = torch.tensor([6.1589, -8.0566, -8.7984]) | |
| else: | |
| raise ValueError("Unknown model name") | |
| if not torch.allclose(logits[0, :3], expected_slice, atol=1e-4): | |
| raise ValueError("Logits don't match") | |
| print("Looks ok!") | |
| if pytorch_dump_folder_path is not None: | |
| Path(pytorch_dump_folder_path).mkdir(exist_ok=True) | |
| print(f"Saving model {model_name} to {pytorch_dump_folder_path}") | |
| model.save_pretrained(pytorch_dump_folder_path) | |
| print(f"Saving feature extractor to {pytorch_dump_folder_path}") | |
| feature_extractor.save_pretrained(pytorch_dump_folder_path) | |
| if push_to_hub: | |
| print("Pushing model and feature extractor to the hub...") | |
| model.push_to_hub(f"MIT/{model_name}") | |
| feature_extractor.push_to_hub(f"MIT/{model_name}") | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| # Required parameters | |
| parser.add_argument( | |
| "--model_name", | |
| default="ast-finetuned-audioset-10-10-0.4593", | |
| type=str, | |
| help="Name of the Audio Spectrogram Transformer model you'd like to convert.", | |
| ) | |
| parser.add_argument( | |
| "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." | |
| ) | |
| parser.add_argument( | |
| "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub." | |
| ) | |
| args = parser.parse_args() | |
| convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub) | |